Dialogue Act Tagging and Segmentation with a Single Perceptron

In this paper we present a simultaneous automatic Dialogue Act (DA) tagger and segmenter.
The model employed is based on the well-known single layer perceptron algorithm used
successfully in other Computational Linguistic tasks. A decoding process was developed for
searching the sequence of segments and DA tags from all the possible exponential
possibilities. A set of features based on combination of words and DA tags were
empirically selected. Models were tested over transcriptions of two corpora of dialogues
(Switchboard and Dihana) and transcriptions and ASR output of a third corpus composed by
meetings (AMI corpus). The results obtained for such a simple but powerful model are for
some of the evaluation metrics equal or better than much more complex models presented in
recent studies for the same experiments.